Jabeen-Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan 58 Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan Munazza Jabeen and Saud Ahmad Khan Int. Institute of Islamic Economics and National University of Sciences and Tech. ABSTRACT What drives volatility in foreign exchange market in Pakistan? This paper undertakes an analysis of modelling exchange rate volatility in Pakistan by potential macroeconomic fundamentals well-known in the economic literature. For this monthly data on Pak Rupee exchange rates in the terms of major currencies (US Dollar, British Pound, Canadian Dollar and Japanese Yen) and macroeconomics fundamentals is taken from April, 1982 to November, 2011. The results show that the PKR-USD exchange rate volatility is influenced by real output volatility, foreign exchange reserves volatility, inflation volatility and productivity volatility. The PKR-GBP exchange rate volatility is influenced by foreign exchange reserves volatility and terms of trade volatility. The PKR-CAD exchange rate volatility is influenced by terms of trade volatility. The findings of this paper reveal that exchange rate volatility in Pakistan results from real shocks than nominal shocks. Key words: Exchange Rate Volatility, GARCH JEL Classifications: F31, C22 1. INTRODUCTION Modelling exchange rate volatility continues to attract attention from both academic and policy researchers due to its significance for the economy. In spite of considerable amount of empirical work undertaken, the modelling volatility in exchange rates remains a challenge. Using information in macroeconomic fundamentals to model volatility in foreign exchange markets is not completely new to the literature (see e.g. Calderón, 2004; Grydaki and Fountas, 2009; Cheung and Lai, 2009; Chipili, 2012) but there is no general conclusion on modelling exchange rates volatility by macroeconomic fundamentals due to the divergent theoretical exchange rate determination models are found in economic literature. There are some studies that have emphasized the importance of nominal shocks with transitory effects on exchange rates volatility (Morana, 2009), while others has documented real shocks with large permanent effects as the dominant source of exchange rate volatility (Bayoumi and Eichengreen, 1998; Devereux and Lane, 2003). Moreover, there are some studies that have shown no connection between macroeconomic fundamentals and exchange rates (Flood and Rose, 1995). Exchange rate volatility in developing countries like Pakistan is very pervasive. In Pakistan, an extensive increase in the exchange rate volatility is seen in the recent years. It has significant effects on decisions made by many economic agents who participate in foreign exchange markets like traders, investors, managers and firms. It also has significant effects on decisions made by policy makers in formulating suitable policies. Therefore, understanding Munazza Jabeen, International Institute of Islamic Economics, International Islamic University Islamabad, Pakistan, (email: [email protected]or [email protected]). Saud Ahmad Khan, National University of Sciences and Technology ([email protected]).
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Jabeen-Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan
58
Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan
Munazza Jabeen
and Saud Ahmad Khan
Int. Institute of Islamic Economics and National University of Sciences and Tech.
ABSTRACT
What drives volatility in foreign exchange market in Pakistan? This paper undertakes an analysis of modelling exchange rate volatility in Pakistan by potential macroeconomic
fundamentals well-known in the economic literature. For this monthly data on Pak Rupee
exchange rates in the terms of major currencies (US Dollar, British Pound, Canadian Dollar and Japanese Yen) and macroeconomics fundamentals is taken from April, 1982 to
November, 2011. The results show that the PKR-USD exchange rate volatility is
influenced by real output volatility, foreign exchange reserves volatility, inflation
volatility and productivity volatility. The PKR-GBP exchange rate volatility is influenced by foreign exchange reserves volatility and terms of trade volatility. The PKR-CAD
exchange rate volatility is influenced by terms of trade volatility. The findings of this
paper reveal that exchange rate volatility in Pakistan results from real shocks than nominal shocks.
Table 4.6 GARCH (1,1) - Nyblom Test for Parameter Stability
Notes: For individual statistics 1% and 5% critical values = 0.75 and 0.47. For joint statistics 1% and 5 % critical
values = 5.13 and 4.52
Parameters PAK-USD PAK-GBP PAK-CAD PAK-JPY
Cst(M) 0.56066 0.03142 0.03022 0.31811
RRY(M) 0.32737 0.03358 0.09467 0.10255
FXR (M) 0.07442 0.04103 0.03503 0.08161
INFD (M) 0.06366 0.12992 0.06658 0.12514
TOT (M) 0.02233 0.09125 0.07143 0.15907
PR (M) 0.49481 0.12026 0.11338 0.18749
AR(1) 0.08277 0.07192 0.13442 0.08790
AR(2) 0.52428 0.16316 0.54792
AR(3) 0.08231
AR(4) 0.28345
Cst(V) 0.36713 0.07998 0.19114 0.13078
VRRY (V) 0.27962 0.07775 0.12987 0.13079
VFXR (V) 0.25780 0.00874 0.09529 0.04545
VINFD (V) 0.40552 0.08870 0.30957 0.06857
VTOT (V) 0.60219 0.08207 0.08769 0.03294
VPR (V) 0.26666 0.05766 0.13628 0.13661
ARCH(Alpha1) 0.69484 0.13421 0.28397 0.08979
GARCH(Beta1) 0.42969 0.08446 0.45395 0.13822
GJR(Gamma1) 0.05861 0.12846 0.17533 0.09942
Joint Lc 4.88737 2.51159 1.90455 4.34341
Table 4.7 GJR- GARCH (1,1)- Nyblom Test for Parameter Stability Notes: For individual statistics 1% and 5% critical values = 0.75 and 0.47.For joint statistics 1% and 5 % critical
values = 5.13 and 4.52
4.3 In-sample Forecasting Evaluation
In order to see which model best describe the data, in-sample forecasting performance is
generated. The Table 4.9 presents the in- sample exchange rate volatility forecasts errors for
the Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error
(RMSE) criteria. The MSE and RMSE criteria show GARCH (1,1) model in all exchange
rates performs best in in- sample forecasting.
International Econometric Review (IER)
73
Models
Specifications
AIC
SIC
Log
Likelihood
Chi-Square
Statistics
GARCH(1,1)
PKR-USD -6.180688 -6.054615 1036.994 39.5663
PKR-GBP -4.474330 -4.354731 756.739 37.6386
PKR-CAD -5.245487 -5.085029 887.548 36.6747
PKR-JPY -4.334571 -4.209958 735.848 36.6747
GJR-GARCH(1,1)
PKR-USD -6.143054 -5.959674 1035.747 28.9639
PKR-GBP -4.460386 -4.351476 755.424 25.8313
PKR-CAD -5.244267 -5.049426 884.751 41.4940
PKR-JPY -4.320349 -4.182814 731.178 37.1566
Table 4.8 Model Selection Criteria and Goodness-of-Fit Test
Specifications
Mean Squared Error
(MSE)
Mean Absolute Error
(MAE)
Root Mean Squared Error
(RMSE)
GARCH(1,1)
PKR-USD 4.11e-009 6.346e-005 6.411e-005
PKR-GBP 9.441e-007 0.0008732 0.0009716
PKR-CAD 1.332e-007 0.0003143 0.0003649
PKR-JPY 3.855e-007 0.0006039 0.0006208
GJR-GARCH(1,1)
PKR-USD 3.98e-009 6.268e-005 6.309e-005
PKR-GBP 9.891e-007 0.0008802 0.0009945
PKR-CAD 1.332e-007 0.0003098 0.000365
PKR-JPY 4.258e-007 0.0003941 0.0006525
Table 4.9 Evaluation of the In-sample Volatility Forecasts
5. CONCLUSION AND POLICY RECOMMENDATIONS
This paper empirically investigates the volatility of Pak Rupee exchange rates GARCH
models using macroeconomic fundamentals. The results show that Pak Rupee exchange rates
are characterized by different dynamics of conditional volatility and conditional volatility in
Pak Rupee exchange rates are affected by different factors indicating variations across
exchange rates in terms of the factors driving conditional volatility. The PKR-USD exchange
rate volatility is influenced by real output volatility, foreign exchange reserves volatility,
inflation volatility and productivity volatility. The PKR-GBP exchange rate volatility is
influenced by foreign exchange reserves volatility and terms of trade volatility. The PKR-
CAD exchange rate volatility is influenced by terms of trade volatility.
The findings of this study reveal the important macroeconomic fundamentals that are
potential sources of exchange rate volatility in Pakistan. The instability in these
macroeconomic fundamentals causes variability in the exchange rates in Pakistan. In addition,
exchange rate volatility in Pakistan results from real shocks than nominal shocks. The role for
exchange rate stabilization is identified. Therefore policy and decision makers need to pay
attention to exchange rate stability. For the achievement of exchange rate stability, it is vital
to realize these macroeconomic fundamentals affecting the exchange rates volatility. In other
words, by controlling these macroeconomic fundamentals, they are able to stabilize
fluctuations in exchange rates. They should provide good strategic policies for the exchange
rate market and develop mechanisms to manage with various shocks. The government
policies should be design in a way that would able to moderate fluctuations in exchange rates.
Wanaset (2001) has pointed out an experience of stability in exchange rates in Singapore
Jabeen-Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan
74
which confirmed that government policies encouraged exchange rate stability through the
strong institutional setup which includes credible price stability, fiscal discipline, considerable
openness and transparency and well-developed capital markets.
Therefore, policy and decision makers in Pakistan should design and develop set of policies
and instruments for the exchange rate stabilization and strengthening of whole financial
system. An efficient financial system leads to efficient intermediation of financial flows. This
in turn reduces fluctuations in the exchange rates and enlarges the economy’s resistance to
shocks. Further, policy and decision makers should pursue close monitoring of the financial
system and develop warning systems for emergence of risks and vulnerabilities. Moreover,
for healthy economics foundations, there is a need to strengthen macroeconomic policies
which encourage macroeconomic balance and lower exposure to speculative movements in
currencies.
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